Objective identification of pressure wave events from networks of 1Hz, high-precision sensors

Abstract

Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8Pa noise floor, 1Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120min were analyzed - a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE<90s and NRMSE<0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.